Jobs / United States / Humana INC

Humana INC

Director, Quality Engineering

5 Locations Posted today

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Skills this job asks for

ComplianceLeadershipCI/CD

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Sponsorship verdict

Sponsorship possible

One solid signal, not two. Worth applying, and worth asking about sponsorship early.

  • Employer is on a government sponsor recordThe US Department of Labor certified 260 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first.
  • The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
  • No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
  • What Humana INC paid sponsored hires in similar roles1 certified filing for “Director, Data Engineering” (Computer and Information Systems Managers) in NY: $261k–$261k, median $261k.
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
Employer is on a government sponsor record
The US Department of Labor certified 260 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
No salary bar for this route
H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
What Humana INC paid sponsored hires in similar roles
1 certified filing for “Director, Data Engineering” (Computer and Information Systems Managers) in NY: $261k–$261k, median $261k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).

US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).

A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.

About the role

Become a part of our caring community   Humana is committed to helping people achieve their best health by delivering innovative technology solutions that enable exceptional member and provider experiences. The Director, Quality Engineering – Test Data Management (EQE) is a senior technology leader responsible for defining, advancing, and operating Humana's enterprise Test Data Management (TDM) function within Enterprise Quality Engineering. Reporting to the VP, Quality Engineering, this role leads the strategy, operating model, and execution of secure, scalable, compliant, and automation-enabled test data capabilities that support software delivery across Humana's business and technology organizations. This leader is accountable for building and leading a high-performing organization responsible for core TDM services, including test data provisioning, synthetic data generation, masking, de-identification, data lifecycle management, and environment data coordination. The Director will partner closely with Quality Engineering, Engineering, Product, Architecture, DevOps, Infrastructure, Security, Privacy, Risk, and Data teams to ensure test data is available, fit for purpose, and aligned to delivery, compliance, and release readiness needs. The ideal candidate brings deep expertise in test data management, data engineering, governance, privacy, and automation in a complex enterprise environment. This role will drive modernization of TDM practices through automation-first and AI-enabled capabilities, while establishing standards, controls, and reusable services that improve engineering throughput, reduce delivery friction, and strengthen compliance and data protection across non-production environments. Responsibilities Leadership, Strategy & Operating Model • Lead the enterprise Test Data Management function within EQE, including strategic direction, operating model execution, and organizational development. • Build, lead, and develop a high-performing team of managers, engineers, and test data specialists across multiple groups supporting segment and enterprise needs. • Oversee a distributed organization of 50+ associates and create clear priorities, role clarity, and performance expectations. • Partner with Quality Engineering, Engineering, Product, Architecture, DevOps, Infrastructure, Security, and Data teams to align TDM services with business and delivery priorities. • Drive a scalable support model for segment TDM services across multiple portfolios and delivery teams. • Promote a culture of quality, compliance, continuous improvement, and shared ownership of secure test data practices. Test Data Engineering & Management • Lead enterprise engineering and management of secure, scalable, and compliant test data solutions. • Oversee delivery of core TDM capabilities, including:Test data provisioningSynthetic data generationData masking and obfuscationData de-identificationData quality enablementTest data automationData lifecycle managementEnvironment data coordination • Ensure test data is available, fit for purpose, and aligned to quality engineering, release, and environment needs. • Advance data engineering practices that improve the reliability, reusability, and speed of test data provisioning across delivery pipelines. • Support complex testing needs across functional, integration, regression, end-to-end, and non-production validation use cases. AI-Enabled & Automation-First TDM • Drive AI-enabled and automation-first TDM strategies to improve speed, intelligence, scalability, and coverage of test data operations. • Expand the use of AI and advanced automation for synthetic data generation, sensitive data detection, masking rule optimization, provisioning efficiency, and data quality insights. • Integrate

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